心脏早博分类的支持向量机模型(英文)

临床上,由于心电图特征信息的交错而难以对患者的心脏早博类型进行正确识别.作为计算机辅助的一种方法,基于从临床收集到的82个患者的样本,建立了支持向量机模型.该模型的训练准确度为94.44%、测试准确度达到92.86%,其留一法交叉检验准确度为92.59%.满意的结果表明所建议的模型可以应用于临床辅助诊断.

An support vector machine model for classification of premature cardiac contractions

It is difficult to determinate the premature cardiac contraction type of a case in clinic due to its vague signals in electrocardiogram.As an approach of computer-assisted diagnosis,a model for classification was proposed based on support vector machine(SVM).All samples data were derived from82 clinic cases.By means of our SVM model,the accuracies of classification were up to 94.44% for the training set and 92.86% for the testing set.The accuracy of leave-one-out cross-validation was 92.59%.The satisfactory results indicate that the proposed approach is effective and could be applied to assisted diagnosis in clinic practice.